Clustering criteria in multiobjective data clustering

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We consider the choice of clustering criteria for use in multiobjective data clustering. We evaluate four different pairs of criteria, three employed in recent evolutionary algorithms for multiobjective clustering, and one from Delattre and Hansen's seminal exact bicriterion method. The criteria pairs are tested here within a single multiobjective evolutionary algorithm and representation scheme to isolate their effects from other considerations. Results on a range of data sets reveal significant performance differences, which can be understood in relation to certain types of challenging cluster structure, and the mathematical form of the criteria. A performance advantage is generally found for those methods that make limited use of cluster centroids and assess partitionings based on aggregate measures of the location of all data points. © 2012 Springer-Verlag.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
Place of PublicationBerlin/Heidelberg
PublisherSpringer Nature
Number of pages9
ISBN (Print)9783642329630
Publication statusPublished - 2012
Event12th International Conference on Parallel Problem Solving from Nature, PPSN 2012 - Taormina, Italy
Duration: 1 Sept 20125 Sept 2012


Conference12th International Conference on Parallel Problem Solving from Nature, PPSN 2012


  • multiobjective clustering, bicriterion clustering, clustering objectives, multiple clustering objectives


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